Questionnaire Data Analysis UK Using SPSS
Questionnaire data analysis UK projects often fail because researchers jump directly to hypothesis tests before checking how items were coded and scored. A reliable SPSS workflow begins with the questionnaire itself, builds a clear codebook, audits the data, constructs scales and only then selects statistical tests.
1. Build a Questionnaire Codebook
Record each item name, label, response options, missing-value code, measurement level and scoring rule. Mark reverse-worded questions clearly. A codebook prevents inconsistent coding and makes later revisions easier.
See How to Create a Codebook in SPSS.
2. Clean the Responses
Check impossible codes, duplicate records, incomplete cases, straight-lining or other data-quality concerns where relevant, and missing values. Do not silently delete unusual responses; document the rule used.
3. Reverse-Code and Compute Scales
For multi-item measures, apply the published scoring guidance. Reverse-code negatively keyed items before computing totals or means, and save those transformations as syntax.
4. Assess Reliability Appropriately
Cronbach’s alpha can be useful for internal consistency, but it should be interpreted alongside item-total correlations, scale content and the intended construct. A high alpha does not by itself prove validity.
Use Cronbach’s Alpha in SPSS and Questionnaire Reliability and Validity in SPSS.
5. Describe the Sample and Responses
Report frequencies and percentages for categorical items and suitable descriptive statistics for scale scores. For Likert data, be clear whether you are describing individual ordinal items or a composite score.
6. Select Tests From the Research Question
Questionnaire studies may use correlation, t-tests, ANOVA, chi-square, regression, factor analysis or non-parametric procedures. Choose based on the outcome, predictors, design and measurement properties.
7. Interpret the Evidence
Explain the direction and magnitude of relationships or group differences, uncertainty and limitations. Avoid treating every p-value below a threshold as an important result.
8. Report a Reproducible Workflow
Keep the raw export, analysis-ready file, syntax, codebook and labelled output. This provides a clear audit trail if a supervisor asks for a revised scale or additional analysis.
Explore Survey Data Analysis in SPSS, Reproducible SPSS Analysis UK and the UK SPSS Statistics Knowledge Hub.